With industrial internet shaping the future, it is only natural to have connected machines forming part of every aspect of technology. Traditionally, predictive reliability mining has been based on historical data on part failures from warranty claims using distributions from exponential family such as the Weibull or log-normal distribution. When observed failures (in one or more parts) across a population of machines exceeds the number expected based on such a model, this may serve as an early warning of a potential systemic problem with the population. Such early warnings rely on some exceptionally high failures having actually occurred. Again, it has been seen that significant deviations from expected failure counts may often occur only in some unknown subset of the population, for instance, a particular batch, or machines manufactured in a particular year or at a particular plant site, and the like. Such deviations are insignificant across the full population and remain unidentified when traditional reliability mining techniques are employed. It is a challenge to not only detect potential problems earlier than possible using traditional reliability analysis but also to identify a subset of the population wherein an anomaly may have occurred that would statistically be otherwise hidden in the population.